Although land use/cover maps are widely used to support management and environmental policies, only some studies have reported their accuracy using sound and complete assessments. Thematic map accuracy assessment is typically achieved by comparing reference sites labeled with the “ground-truth” category to the ones depicted in the land use/cover map. A variety of sampling designs are used to select these references sites. The estimators for accuracy indices and the variance of these estimators depend on the sampling design. However, the tools used to assess accuracy available in the main program packages compute the accuracy indices without taking into account the sampling and give inconsistent estimates. As an alternative, we present free user-friendly tools that enable users beyond the Geographic Information Science Community to compute accuracy indices and estimate corrected areas of given categories with their respective confidence intervals. The tool runs in Dinamica EGO, a free platform for environmental spatial modeling as well as a Q-GIS plugin and a R package. Additionally, a practical application example is described using a case study area in central-west Mexico. 1. Introduction Thematic maps such as land use/cover maps are widely used to support management and environmental policies and therefore they should be supported by a statistically rigorous, credible accuracy assessment [1, 2]. Thematic accuracy is a measure of correctness that can be defined as the degree to which the attributes of a map agree with “truth” reference datasets. Accuracy assessment is typically based on a sample of reference sites to which the “true” land use/cover category is compared to the one in the map. A variety of sampling designs can be used to select these references sites (sample units). The objectives, the desirable criteria, and the resources of the assessment have to be taken into account to choose the sampling design. First of all, the sampling design should be a probability sampling design, which means that the sample unit is selected randomly; the inclusion probability for each sample unit into the sample is known and must be greater than zero for all the units in the area under assessment. Probability sampling enables statistical inference allowing the computing of accuracy estimates along with their confidence intervals. Convenient procedures such as selecting training data used during supervised classification or by limiting the random sampling of reference sites to accessible sites or area covered by available high resolution images do not fulfill
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